{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:IZI7Q24BSAUZ7OLTWCXVCKKFIQ","short_pith_number":"pith:IZI7Q24B","schema_version":"1.0","canonical_sha256":"4651f86b8190299fb973b0af5129454421c177c1e532b7457a854c80bfedb8cb","source":{"kind":"arxiv","id":"2506.24042","version":2},"attestation_state":"computed","paper":{"title":"Faster Diffusion Models via Higher-Order Approximation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NA","math.NA","math.ST","stat.ML","stat.TH"],"primary_cat":"cs.LG","authors_text":"Gen Li, Yuchen Zhou, Yuting Wei, Yuxin Chen","submitted_at":"2025-06-30T16:49:03Z","abstract_excerpt":"In this paper, we explore provable acceleration of diffusion models without any additional retraining. Focusing on the task of approximating a target data distribution in $\\mathbb{R}^d$ to within $\\varepsilon$ total-variation distance, we propose a principled, training-free sampling algorithm that requires only the order of\n  $$ d^{1+2/K} \\varepsilon^{-1/K} $$\n  score function evaluations (up to log factor) in the presence of accurate scores, where $K>0$ is an arbitrary fixed integer. This result applies to a broad class of target data distributions, without the need for assumptions such as sm"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2506.24042","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-30T16:49:03Z","cross_cats_sorted":["cs.NA","math.NA","math.ST","stat.ML","stat.TH"],"title_canon_sha256":"6f99aecf431ac74427a72979afda299085449cbcdbbcd0368d74f59584c3f388","abstract_canon_sha256":"cb02b8870904271bdce8f2dd273769c355006d4dfda2d4f2d7b808f33effd147"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:53:04.845209Z","signature_b64":"Ye9H8xLbtLb48tXcElBhCP8DWGrDw3wB9LQIUAGdA4zuwrfmW4ORv+m914GY4JvbxNLvSx4T1rk19dJ5TUBQCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4651f86b8190299fb973b0af5129454421c177c1e532b7457a854c80bfedb8cb","last_reissued_at":"2026-07-05T11:53:04.844758Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:53:04.844758Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Faster Diffusion Models via Higher-Order Approximation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NA","math.NA","math.ST","stat.ML","stat.TH"],"primary_cat":"cs.LG","authors_text":"Gen Li, Yuchen Zhou, Yuting Wei, Yuxin Chen","submitted_at":"2025-06-30T16:49:03Z","abstract_excerpt":"In this paper, we explore provable acceleration of diffusion models without any additional retraining. Focusing on the task of approximating a target data distribution in $\\mathbb{R}^d$ to within $\\varepsilon$ total-variation distance, we propose a principled, training-free sampling algorithm that requires only the order of\n  $$ d^{1+2/K} \\varepsilon^{-1/K} $$\n  score function evaluations (up to log factor) in the presence of accurate scores, where $K>0$ is an arbitrary fixed integer. This result applies to a broad class of target data distributions, without the need for assumptions such as sm"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.24042","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2506.24042/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2506.24042","created_at":"2026-07-05T11:53:04.844814+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.24042v2","created_at":"2026-07-05T11:53:04.844814+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.24042","created_at":"2026-07-05T11:53:04.844814+00:00"},{"alias_kind":"pith_short_12","alias_value":"IZI7Q24BSAUZ","created_at":"2026-07-05T11:53:04.844814+00:00"},{"alias_kind":"pith_short_16","alias_value":"IZI7Q24BSAUZ7OLT","created_at":"2026-07-05T11:53:04.844814+00:00"},{"alias_kind":"pith_short_8","alias_value":"IZI7Q24B","created_at":"2026-07-05T11:53:04.844814+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.23627","citing_title":"Diffusion Models Adapt to Low-Dimensional Structure Under Flexible Coefficient Choices","ref_index":35,"is_internal_anchor":false},{"citing_arxiv_id":"2606.10353","citing_title":"Higher-order Diffusion Sampling via Chebyshev Interpolation and Gauss--Seidel Iterations","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"2606.03820","citing_title":"A Quantitative Approximation Framework for Flow Distillation in Diffusion Models","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"2605.27352","citing_title":"From Scores to Gibbs Correctors: Accelerating Uniform-Rate Discrete Diffusion Models","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07220","citing_title":"On the Robustness of Distribution Support under Diffusion Guidance","ref_index":78,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07220","citing_title":"On the Robustness of Distribution Support under Diffusion Guidance","ref_index":78,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IZI7Q24BSAUZ7OLTWCXVCKKFIQ","json":"https://pith.science/pith/IZI7Q24BSAUZ7OLTWCXVCKKFIQ.json","graph_json":"https://pith.science/api/pith-number/IZI7Q24BSAUZ7OLTWCXVCKKFIQ/graph.json","events_json":"https://pith.science/api/pith-number/IZI7Q24BSAUZ7OLTWCXVCKKFIQ/events.json","paper":"https://pith.science/paper/IZI7Q24B"},"agent_actions":{"view_html":"https://pith.science/pith/IZI7Q24BSAUZ7OLTWCXVCKKFIQ","download_json":"https://pith.science/pith/IZI7Q24BSAUZ7OLTWCXVCKKFIQ.json","view_paper":"https://pith.science/paper/IZI7Q24B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.24042&json=true","fetch_graph":"https://pith.science/api/pith-number/IZI7Q24BSAUZ7OLTWCXVCKKFIQ/graph.json","fetch_events":"https://pith.science/api/pith-number/IZI7Q24BSAUZ7OLTWCXVCKKFIQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IZI7Q24BSAUZ7OLTWCXVCKKFIQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IZI7Q24BSAUZ7OLTWCXVCKKFIQ/action/storage_attestation","attest_author":"https://pith.science/pith/IZI7Q24BSAUZ7OLTWCXVCKKFIQ/action/author_attestation","sign_citation":"https://pith.science/pith/IZI7Q24BSAUZ7OLTWCXVCKKFIQ/action/citation_signature","submit_replication":"https://pith.science/pith/IZI7Q24BSAUZ7OLTWCXVCKKFIQ/action/replication_record"}},"created_at":"2026-07-05T11:53:04.844814+00:00","updated_at":"2026-07-05T11:53:04.844814+00:00"}